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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Predicting Fluid Flow Regime, Permeability, and Diffusivity in Mudrocks from Multiscale Pore Characterisation

In geoenergy applications, mudrocks prevent fluids to leak from temporary (H 2 , CH 4 ) or permanent (CO 2 , radioactive waste) storage/disposal sites and serve as a source and reservoir for unconventional oil and gas. Understanding transport properties integrated with dominant fluid flow mechanisms in mudrocks is essential to better predict the performance of mudrocks within these applications. In this study, small-angle neutron scattering (SANS) experiments were conducted on 71 samples from 13 different sets of mudrocks across the globe to capture the pore structure of nearly the full pore size spectrum (2 nm–5 μm). We develop fractal models to predict transport properties (permeability and diffusivity) based on the SANS-derived pore size distributions. The results indicate that transport phenomena in mudrocks are intrinsically pore size-dependent. Depending on hydrostatic pore pressures, transition flow develops in micropores, slip flow in meso- and macropores, and continuum flow in larger macropores. Fluid flow regimes progress towards larger pore sizes during reservoir depletion or smaller pore sizes during fluid storage, so when pressure is decreased or increased, respectively. Capturing the heterogeneity of mudrocks by considering fractal dimension and tortuosity fractal dimension for defined pore size ranges, fractal models integrate apparent permeability with slip flow, Darcy permeability with continuum flow, and gas diffusivity with diffusion flow in the matrix. This new model of pore size-dependent transport and integrated transport properties using fractal models yields a systematic approach that can also inform multiscale multi-physics models to better understand fluid flow and transport phenomena in mudrocks on the reservoir and basin scale.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Response Of Mineralizing And Non-Mineralizing Bone Cells To Fluid Flow: An In Vitro Model For Mechanotransruction

Humans reach peak bone mass at age 30. After this point, we lose 1 to 2 percent of bone mass each decade. In the microgravity environment of space, astronauts lose bone mass at an accelerated rate of 1 to 2 percent each month. When astronauts travel to Mars, they may be in space for as long as 3 years. During this time, they may lose about half of their bone mass from weight-bearing bones. This loss may be irreversible. The drastic loss in bone that astronauts experience in space makes them much more vulnerable to fractures. In addition, the corresponding removal of calcium from bone results in higher levels of calcium in the blood, which increases the risk of developing kidney stones. Currently, studies are being conducted which investigate factors governing bone adaptation and mechanotransduction. Bone is constantly adapting in response to mechanical stimuli. Increased mechanical loading stimulates bone formation and suppresses bone resorption. Reduction in mechanical loading caused by bedrest, disuse, or microgravity results in decreased bone formation and possibly increased bone resorption. Osteoblasts and osteoclasts are the two main cell types that participate in bone remodeling. Osteoblasts are anabolic (bone-forming) cells and osteoclasts are catabolic (bone-resorbing) cells. In microgravity, the activity of osteoblasts slows down and the activity of osteoclasts may speed up, causing a loss of bone density. Mechanotransduction, the molecular mechanism by which mechanical stimuli are converted to biochemical signals, is not yet understood. Exposure of cells to fluid flow imposes a shear stress on the cells. Several studies have shown that the shear stress that results from fluid flow induces a cellular response similar to that induced by mechanical loading. Thus, fluid flow can be used as an in vitro model to simulate the mechanical stress that bone cells experience in vivo. Previous in vitro studies have shown that fluid flow induces several responses in osteoblasts, including increased proliferation, osteoblastic differentiation, alkaline phosphatase activity, and production of nitric oxide, prostaglandins, and osteopontin. Several proteins have been implicated in osteoblastic mechanotransduction including Bone Morphogenetic Protein-2 (BMP-2), parathyroid hormone, 1,25-dihydroxyvitamin D3 receptor, osteopontin (OPN), osteoprotegerin (OPG), and alkaline phosphatase (AP). We will characterize relative levels of each protein in mineralizing or non-mineralizing MC3T3 osteoblastic cells that have been exposed to fluid flow compared to non-fluid flow using immunofluorescent staining and two- photon laser microscopy as well as western blotting. Because calcium-mediated pathways are important in osteoblastic signaling, we will transfect MC3T3 cells with cameleon probes for Ca2+ containing YFP and CFP. Results will be analyzed using FRET/FLIM to study differential release of intracellular Ca(2+) in response to fluid flow and conditions inducing matrix mineralization. In addition, we plan to conduct several microarray experiments to determine differential gene expression in MC3T3 cells in response to fluid flow and conditions inducing mineralization.

Makuch, Lauren A.↗
NASA NTRS1991

GEOSIM: A numerical model for geophysical fluid flow simulation

A numerical model which simulates geophysical fluid flow in a wide range of problems is described in detail, and comparisons of some of the model's results are made with previous experimental and numerical studies. The model is based upon the Boussinesq Navier-Stokes equations in spherical coordinates, which can be reduced to a cylindrical system when latitudinal walls are used near the pole and the ratio of latitudinal length to the radius of the sphere is small. The equations are approximated by finite differences in the meridional plane and spectral decomposition in the azimuthal direction. The user can specify a variety of boundary and initial conditions, and there are five different spectral truncation options. The results of five validation cases are presented: (1) the transition between axisymmetric flow and baroclinic wave flow in the side heated annulus; (2) the steady baroclinic wave of the side heated annulus; (3) the wave amplitude vacillation of the side heated annulus; (4) transition to baroclinic wave flow in a bottom heated annulus; and (5) the Spacelab Geophysical Fluid Flow Cell (spherical) experiment.

Butler, Karen A.↗
NASA NTRS1999

The Geophysical Fluid Flow Cell Experiment

The Geophysical Fluid Flow Cell (GFFC) experiment performed visualizations of thermal convection in a rotating differentially heated spherical shell of fluid. In these experiments dielectric polarization forces are used to generate a radially directed buoyancy force. This enables the laboratory simulation of a number of geophysically and astrophysically important situations in which sphericity and rotation both impose strong constraints on global scale fluid motions. During USML-2 a large set of experiments with spherically symmetric heating were carried out. These enabled the determination of critical points for the transition to various forms of nonaxisymmetric convection and, for highly turbulent flows, the transition latitudes separating the different modes of motion. This paper presents a first analysis of these experiments as well as data on the general performance of the instrument during the USML-2 flight.

Hart, J. E.↗
NASA NTRS2017

Water Hammer Simulations of MMH Propellant - New Capability Demonstration of the Generalized Fluid Flow Simulation Program

Fluid Transient analysis is important for the design of spacecraft propulsion system to ensure structural stability of the system in the event of sudden closing or opening of the valve. Generalized Fluid System Simulation Program (GFSSP), a general purpose flow network code developed at NASA/MSFC is capable of simulating pressure surge due to sudden opening or closing of valve when thermodynamic properties of real fluid are available for the entire range of simulation. Specifically GFSSP needs an accurate representation of pressure-density relationship in order to predict pressure surge during a fluid transient. Unfortunately, the available thermodynamic property programs such as REFPROP, GASP or GASPAK does not provide the thermodynamic properties of Monomethylhydrazine (MMH). This paper will illustrate the process used for building a customized table of properties of state variables from available properties and speed of sound that is required by GFSSP for simulation. Good agreement was found between the simulations and measured data. This method can be adopted for modeling flow networks and systems with other fluids whose properties are not known in detail in order to obtain general technical insight. Rigorous code validation of this approach will be done and reported at a future date.

Burkhardt, Z.↗
NASA NTRS1970

Simplified computation of compressible fluid flow parameters

Mathematical techniques analyze fluid flow through circular duct, enlarged or constricted circular duct, and across an orifice or nozzle. Using subsonic Mach number as basic flow parameter, a series of working graphs displays the variation of various physical parameters in each of the flow conditions.

Beimgraben, H. W.↗
DOE OSTI2024

The Impact of Wettability on the Co-moving Velocity of Two-Fluid Flow in Porous Media

Abstract The impact of wettability on the co-moving velocity of two-fluid flow in porous media is analyzed herein. The co-moving velocity, developed by Roy et al. (Front Phys 8:4, 2022), is a novel representation of the flow behavior of two fluids through porous media. Our study aims to better understand the behavior of the co-moving velocity by analyzing simulation data under various wetting conditions. We analyzed 46 relative permeability curves based on the Lattice–Boltzmann color fluid model and two experimentally determined relative permeability curves. The analysis of the relative permeability data followed the methodology proposed by Roy et al. (Front Phys 8:4, 2022) to reconstruct a constitutive equation for the co-moving velocity. Surprisingly, the coefficients of the constitutive equation were found to be nearly the same for all wetting conditions. On the basis of these results, a simple approach was proposed to reconstruct the relative permeability of the oil phase using only the co-moving velocity relationship and the relative permeability of the water phase. This proposed method provides new information on the interdependence of the relative permeability curves, which has implications for the history matching of production data and the solution of the associated inverse problem. The research findings contribute to a better understanding of the impact of wettability on fluid flow in porous media and provide a practical approach for estimating relative permeability based on the co-moving velocity relationship, which has never been shown before.

Engineering↗
DOE OSTI2023

Finding a contaminant source in a volume of flowing fluid

A method includes: mapping a boundary of a volume of flowing fluid; partitioning the volume by a computational mesh; finding a contaminant location at a first sensor that is disposed within the volume; obtaining a measured velocity of the flowing fluid within the volume; generating a reversed velocity vector field within the mesh, in response to the measured velocity; time stepping the contaminant location from the first sensor along the reversed velocity vector field until the contaminant location intersects the boundary of the volume; and finding a contaminant source at the intersection of the time stepped contaminant location with the boundary of the volume.

Lopez-Marrero, Vanessa↗
NASA NTRS1995

elVis: An Interactive System For Visualization of Unsteady Fluid Flow

ElVis is a prototype system with allows for the interactive visualization of unsteady fluid flow. The increasing computational power applied to fluid dynamics simulations presents the enormous challenge to the visualization system designer to apply a wide range of technologies to the analysis process with ever increasing demands on performance. Visualization of the results of unsteady fluid flow simulations presents the challenge of exploring very large and complex data sets. Since exploration is a trial and error process, it is of utmost importance that the time required to execute a trial (i.e., create a visualization) be at a minimum in order to provide real time interaction.

Gerald-Yamasaki, Michael↗
NASA NTRS1992

Using artificial intelligence to control fluid flow computations

Computational simulation is an essential tool for the prediction of fluid flow. Many powerful simulation programs exist today. However, using these programs to reliably analyze fluid flow and other physical situations requires considerable human effort and expertise to set up a simulation, determine whether the output makes sense, and repeatedly run the simulation with different inputs until a satisfactory result is achieved. Automating this process is not only of considerable practical importance but will also significantly advance basic artificial intelligence (AI) research in reasoning about the physical world.

Gelsey, Andrew↗
NASA NTRS2014

Generating Inviscid and Viscous Fluid Flow Simulations over a Surface Using a Quasi-simultaneous Technique

A fluid-flow simulation over a computer-generated surface is generated using a quasi-simultaneous technique. The simulation includes a fluid-flow mesh of inviscid and boundary-layer fluid cells. An initial fluid property for an inviscid fluid cell is determined using an inviscid fluid simulation that does not simulate fluid viscous effects. An initial boundary-layer fluid property a boundary-layer fluid cell is determined using the initial fluid property and a viscous fluid simulation that simulates fluid viscous effects. An updated boundary-layer fluid property is determined for the boundary-layer fluid cell using the initial fluid property, initial boundary-layer fluid property, and an interaction law. The interaction law approximates the inviscid fluid simulation using a matrix of aerodynamic influence coefficients computed using a two-dimensional surface panel technique and a fluid-property vector. An updated fluid property is determined for the inviscid fluid cell using the updated boundary-layer fluid property.

Sturdza, Peter↗
DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

On the Existence of Steady-State Solutions to the Equations Governing Fluid Flow in Networks

The steady-state solution of fluid flow in pipeline infrastructure networks driven by junction/node potentials is a crucial ingredient in various decision-support tools for system design and operation. While the nonlinear system is known to have a unique solution (when one exists), the absence of a definite result on the existence of solutions hobbles the development of computational algorithms, for it is not possible to distinguish between algorithm failure and non-existence of a solution. In this letter, we show that for any fluid whose equation of state is a scaled monomial, a unique solution exists for such nonlinear systems if the term solution is interpreted in terms of potentials and flows rather than pressures and flows. However, for gases following the CNGA equation of state, while the question of existence remains open, we construct an alternative system that always has a unique solution and show that the solution to this system is a good approximant of the true solution. Further, the existence result for flow of natural gas in networks also applies to other fluid flow networks such as water distribution networks or networks that transport carbon dioxide in carbon capture and sequestration. Most importantly, our result enables correct diagnosis of algorithmic failure, problem stiffness, and non-convergence in computational algorithms.

42 ENGINEERING↗
DOE OSTI2022

CRADA Number NFE-19-07867 with Neptune Fluid Flow Systems (CRADA Final Report)

Neptune Fluid Flow Systems is a rising engineering firm with a technology focus in improving cryogenic sample preparation for cryogenic-transmission electron microscopy (cryo-TEM) studies. To advance the R&D of its innovative cryogenic preparation method, Neptune has teamed up with the Center for Nanophase Materials Science (CNMS) at Oak Ridge National Laboratory (ORNL) to achieve the following aims: (1) validate the technical feasibility of CryoSheet sample prep method, (2) design, fabricate and test the cryo-stage support, and (3) experiment and achieve success with the novel sample preparation method on different soft matters.

36 MATERIALS SCIENCE↗